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| 1 |
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<!DOCTYPE html>
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| 2 |
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Teachable Machine Image Model</title>
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</head>
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<body>
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<div>Teachable Machine Image Model</div>
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<div id="webcam-container"></div>
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<div id="label-container"></div>
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<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest/dist/tf.min.js"></script>
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<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/image@latest/dist/teachablemachine-image.min.js"></script>
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<script type="text/javascript">
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const URL = "https://teachablemachine.withgoogle.com/models/ZPfAhDYCh/";
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let model, webcam, labelContainer, maxPredictions;
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async function init() {
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const modelURL = URL + "model.json";
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const metadataURL = URL + "metadata.json";
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model = await tmImage.load(modelURL, metadataURL);
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maxPredictions = model.getTotalClasses();
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const flip = true;
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webcam = new tmImage.Webcam(200, 200, flip);
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await webcam.setup();
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await webcam.play();
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window.requestAnimationFrame(loop);
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document.getElementById("webcam-container").appendChild(webcam.canvas);
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labelContainer = document.getElementById("label-container");
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for (let i = 0; i < maxPredictions; i++) {
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labelContainer.appendChild(document.createElement("div"));
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}
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}
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async function loop() {
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webcam.update();
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await predict();
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window.requestAnimationFrame(loop);
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}
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async function predict() {
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const prediction = await model.predict(webcam.canvas);
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for (let i = 0; i < maxPredictions; i++) {
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const classPrediction = prediction[i].className + ": " + prediction[i].probability.toFixed(2);
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labelContainer.childNodes[i].innerHTML = classPrediction;
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}
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}
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init(); // Automatically start the model when the page loads
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</script>
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</body>
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</html>
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